CN104966406B - A kind of special vehicle information physical emerging system is selected to method - Google Patents
A kind of special vehicle information physical emerging system is selected to method Download PDFInfo
- Publication number
- CN104966406B CN104966406B CN201510232737.0A CN201510232737A CN104966406B CN 104966406 B CN104966406 B CN 104966406B CN 201510232737 A CN201510232737 A CN 201510232737A CN 104966406 B CN104966406 B CN 104966406B
- Authority
- CN
- China
- Prior art keywords
- crossing
- vehicle
- numbering
- special vehicle
- adjacency
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000005516 engineering process Methods 0.000 abstract description 25
- 238000004891 communication Methods 0.000 abstract description 14
- 238000013461 design Methods 0.000 abstract description 6
- 230000003993 interaction Effects 0.000 abstract description 6
- 238000004364 calculation method Methods 0.000 abstract description 5
- 230000001960 triggered effect Effects 0.000 description 4
- 230000006698 induction Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000010365 information processing Effects 0.000 description 2
- 238000002844 melting Methods 0.000 description 2
- 230000008018 melting Effects 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 230000002153 concerted effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
Abstract
The present invention provides a kind of special vehicle information physical emerging system and selects to method, the method comprehensive utilization sensing technology, the communication technology, computing technique and control technology, make one, car, road interaction collaboration, Scenario Design five for special vehicle crowded generation avoids crowded strategy, ensure that the special vehicle with police car, taxi as representative can dynamically be uniformly distributed under the constraint of existing road, form collection and calculate, communicate, be controlled in " overall situationization, intellectuality, mobilism, the automation " of one and select to method.The present invention can improve the dispatching efficiency of special vehicle, introduce the routing factor by crowded strategy is avoided so that special vehicle is visible in all sections.In addition, the inventive method time complexity is low, requirement of real-time of the information physical emerging system towards special vehicle in scheduling process is disclosure satisfy that.
Description
Technical field
The present invention relates to a kind of based on avoiding the special vehicle information physical emerging system of crowded prediction algorithm from selecting to method,
It is mainly used in solving police car, taxi etc. in intelligent transportation system special vehicle is equally distributed under existing road constraint to ask
Topic, and meet the requirement towards information physical emerging system real-time in scheduling process of special vehicle, belong to information thing
The interleaving techniques application field of reason emerging system and intelligent transportation system.
Background technology
Information physical emerging system is considered as the third wave of the world information technology after computer, internet.
Information physical emerging system can be understood as the high-efficiency network intelligent information system based on embedded device, be have height certainly
Main perception, it is autonomous judge, from main regulation and capacity of self-government, can realize that virtual world and real physical world are interconnected and cooperateed with
Intelligence system of future generation.Information physical emerging system functionally mainly considers performance optimization, is that collection is calculated
(Computation), communication (Communication) and control (Control) 3C are in the intellectual technology of one.Now, information
Physics emerging system technology has been obtained for the highest attention of international industrial and commercial circles and many large-scale international corporations, development speed pole
For rapid, the multiple important development such as traffic, medical treatment, energy fields are applied to, had broad application prospects.
Intelligent transportation system is a kind of typical information physical emerging system, and it is by advanced information technology, data communication
Transmission technology, Electronic transducer technology, control technology and computer technology etc. are effectively integrated and apply to whole ground traffic control management
System and set up it is a kind of interior on a large scale, comprehensive play a role, in real time, accurately and efficiently composite communications transport management
System.ITS (Intelligent Transportation System) can effectively utilize existing means of transportation, reduce and hand over
Logical load and environmental pollution, guarantee traffic safety, raising conevying efficiency, thus, it is increasingly subject to the attention of various countries.21 century will be
In highway communication intelligentized century, the intelligent transportation system that people will use is a kind of advanced integrated traffic integrated pipe
Reason system.In the method, vehicle intelligence on one's own account is freely travelled on road, and highway is by the intelligent by the magnitude of traffic flow of itself
Adjust to optimum state, by means of this system, situations such as administrative staff can be grasped to road, vehicle whereabouts.
Special vehicle scheduling system be intelligent transportation system one kind typical case application, the system synthesis using sensing technology,
The communication technology, computing technique and control technology, make one, car, road interaction collaboration, for the Scenario Design of the crowded generation of special vehicle
Avoid crowded strategy, it is ensured that the special vehicle with police car, taxi as representative can be dynamic uniform under the constraint of existing road
Distribution, forms the scheduling system that collection calculated, and communicated, being controlled in " overall situationization, intellectuality, mobilism, the automation " of one, from
And improve the selecting to efficiency of special vehicle.
Crowded prediction algorithm is avoided to select the reason for producing crowded during for special vehicle：(1) two car it is opposite and
OK；(2) two cars accompany and go；(3) two cars run towards same crossing, devise five and avoid crowded strategy, are respectively：(1)
Many cars are avoided to travel strategy in opposite directions；(2) strategy for avoiding many cars from being travelled towards same crossing；(3) minimum routing factor induction strategies；
(4) do not turn around+random routing strategy；(5) shortest path section preference strategy.Special vehicle drive to crossing select to when, according to real-time
Scenario triggered avoid crowded strategy accordingly, crowded generation is evaded in advance, reaching many cars with this cooperates with, and makes each special vehicle
Equally distributed purpose.
The routing factor is to improve what its each section in urban district occurred in special vehicle patrol scheduling process
Frequency (observability) and introduce a parameter.The routing factor is an integer array, the every section correspondence one in urban district
Array element.On each bar section whenever thering is special vehicle to pass through, to should the routing factor in section just add 1.The routing factor
Introducing ensure that there is observability higher on the basis of special vehicle is uniformly distributed, and make the police car can be more during patrol
Widely fright makes taxi to occur on each section and timely responds to the need of the convenient trip of people to offender
Ask.
With the proposition of information physical emerging system and developing rapidly for intelligent transport technology, with police car, taxi as generation
The special vehicle scheduling system of table causes the concern of multi-field expert, it is proposed that many novel methods.As a whole, have two
Class basic thought：The first thought is exactly the scheduling thought as representative with ant group algorithm, such dispatching method by " smell " come
The special vehicle number passed through in corresponding road section is embodied, the size according to " smell " is come programming dispatching scheme；Another thought be with
Gravitation field method is the scheduling thought of representative, gravitation and repulsion are defined first, then according to gravitation between each special vehicle, repulsion
Make a concerted effort come for special vehicle is selected to so as to realize the interaction between vehicle, reaching makes special vehicle dynamically equally distributed purpose.
The content of the invention
Technical problem：The present invention be given it is a kind of towards special vehicle based on avoiding the information physical of crowded prediction algorithm from melting
Syzygy system is selected to method, the method comprehensive utilization sensing technology, the communication technology, computing technique and control technology, make one, car, road
Interaction collaboration, the Scenario Design five for special vehicle crowded generation avoids crowded strategy, it is ensured that with police car, taxi
For the special vehicle for representing can dynamically be uniformly distributed under the constraint of existing road, form collection and calculate, communicate, being controlled in one
" overall situationization, intellectuality, mobilism, automation " select to method, so as to improve the dispatching efficiency of special vehicle.Meanwhile,
Due to introducing the routing factor in crowded strategy is avoided so that special vehicle is visible in all sections.In addition, it is to avoid crowded pre-
The time complexity of method of determining and calculating is low, disclosure satisfy that real-time in scheduling process towards the information physical emerging system of special vehicle
Property require.
Technical scheme：It is of the present invention based on avoiding the special vehicle information physical emerging system of crowded prediction algorithm from selecting
To method apply selected towards the special vehicle with police car, taxi as representative to information physical emerging system in, select to plan
Slightly it is based on avoiding crowded prediction algorithm.Special vehicle is selecting the introducing information physical emerging system during in real time, integrates intelligence
Traffic system, comprehensive utilization sensing technology, the communication technology, computing technique and control technology, make one, car, road interaction collaboration, in spy
Kind of vehicle drive to crossing select to when, crowded strategy is avoided according to real-time scenario triggered accordingly, crowded product is evaded in advance
Raw, being reached with this makes each special vehicle equally distributed purpose of dynamic under the constraint of existing road.
Information physical emerging system of the present invention is by some physical locations (special vehicle, road conditions information gathering equipment
And user terminal) and information process unit (basic data and scheduler module) composition, physical location and information process unit
Communication interface is equipped with, is interacted by communication module.Physical location to information process unit send vehicle location, speed,
Section, vehicle direction, road conditions and call information, receive the control instruction from information process unit where vehicle；Information processing
Unit builds special vehicle Run-time scenario according to the information of vehicles, traffic information and call information combination basic data for receiving,
Each scene is carried out abstract and formulate corresponding strategy, and then scheduling control information is sent in real time to special vehicle.
It is of the present invention based on avoiding the special vehicle information physical emerging system of crowded prediction algorithm from selecting to method bag
Include following steps：
Step 1) read the map datum including crossing coordinate, crossing syntople that user provides, the adjacent crossing of generation
Table array adjacency, creates routing factor array selectionFactor；All crossings in map are compiled successively since 0
Number, an adjacent crossing table element in the corresponding one-dimension array adjacency in each crossing, storage and the road in adjacent crossing table
Mouth adjacent all crossings numbering and adjacent crossing number；The selectionFactor is unsigned long two-dimensional array,
The array was recorded within the cycle on duty that a user specifies, and the number of times of the special vehicle passed through on each bar section in map is each
The corresponding routing factor values in bar section, all elements value is initially 0, and the routing factor pair in every section answers one
SelectionFactor elements, routing factor values are numbered in array by two end points crossings of corresponding road section
Retrieved in selectionFactor, whenever having special vehicle to pass through on a section, the routing factor value in this section just adds
1, after an end cycle on duty, all elements value resets in array selectionFactor, and element value is 0 to arrive in the array
264In an integer.Read the configuration information that user provides, including step-length STEP, the spy that cycle T on duty, special vehicle are moved
The time t needed for vehicle moves a step-length is planted, setting system timer timer is simultaneously initially 0, all special purpose vehicle
Often moving forward step-length a STEP, timer just increases t, when timer is equal to T, that is, when reaching a cycle on duty,
Timer zero setting；
Step 2) obtain the positional information of all special vehicles, residing road section information and include that whole story crossing is numbered and towards letter
Breath, is stored in list specialVehicle；Traversal of lists specialVehical, note drives to the special vehicle at crossing to wait to select
To special vehicle, set first therein and wait to select to special vehicle currently to select to vehicle；
Step 3) read current selecting to the corresponding crossing numbering in vehicle present position；
Step 4) it is the current numbering selected and the target crossing to be sailed for is selected to vehicle, specifically chosen strategy includes：Keep away
Exempt from many cars to travel strategy that is tactful, avoiding many cars from being travelled towards same crossing, routing factor minimum value preference strategy in opposite directions, do not turn around
With random routing strategy, shortest path section preference strategy；
Step 5) in routing factor array selectionFactor, the corresponding routing factor values of target road section are increased
1；When needing to be selected to special vehicle, write down one and wait to select to special vehicle currently to select to vehicle, be transferred to step 3)；When all
Wait to select and all complete to select to all special vehicles move forward step-length STEP, timer a value for user configuring and increase to special vehicle
Plus t, judge the relation of timer and T, if timer is less than T, it is transferred to step 2), otherwise, perform step 6)；
Step 6) end cycle on duty, routing factor array selectionFactor resets, system timer
Timer zero setting.
Wherein：
It is described to avoid many cars from travelling strategy in opposite directions being：
Step 4.1.1) obtain the current adjacent crossing table adjacency selected to crossing residing for vehicle；
Step 4.1.2) obtain other special vehicle institute directions crossing numbering, judge successively these crossings whether be work as
Before select to the crossing residing for vehicle, if so, then search the numbering at corresponding special vehicle starting crossing, from adjacent crossing table
The initial crossing numbering is removed in adjacency；
Step 4.1.3) obtain the current current length for selecting the optional adjacent crossing table adjaceney to crossing residing for vehicle
size；
Step 4.1.4) if size is equal to 0, perform step 4.1.5)；If size is equal to 1, step 4.1.6 is performed)；
If size is more than 1, step 4.1.7 is performed)；
Step 4.1.5) perform step 4.5), obtain target crossing numbering；
Step 4.1.6) remaining only element value in optional adjacency list, i.e. target crossing numbering；
Step 4.1.7) perform step 4.2), obtain target crossing numbering.
The described strategy for avoiding many cars from being travelled towards same crossing is：
Step 4.2.1) all special vehicles are traveled through, whether the numbering for judging the crossing of its direction successively is table
The element of adjacency, if so, then deleting the numbering at the crossing of correspondence vehicle institute direction from table adjacency；
Step 4.2.2) obtain the current current length for selecting the optional adjacent crossing table adjaceney to crossing residing for vehicle
size；
Step 4.2.3) if size is equal to 0, perform step 4.2.4)；If size is equal to 1, step 4.2.5 is performed)；
If size is more than 1, step 4.2.6 is performed)；
Step 4.2.4) perform step 4.5), obtain target crossing numbering；
Step 4.2.5) remaining only element value in optional adjacency list, i.e. target crossing numbering；
Step 4.2.6) perform step 4.3), obtain target crossing numbering.
Described routing factor minimum value preference strategy is：
Step 4.3.1) the routing factor of all respective stretch in inquiry table adjacency, contrast to obtain the minimum routing factor
minFactor；
Step 4.3.2) delete the end points road that the corresponding routing factor in table adjacency is more than the section of minFactor
Mouth numbering；
Step 4.3.3) obtain the current current length for selecting the optional adjacent crossing table adjaceney to crossing residing for vehicle
size；
Step 4.3.4) if size is equal to 0, perform step 4.3.5)；If size is equal to 1, step 4.3.6 is performed)；
If size is more than 1, step 4.3.7 is performed)；
Step 4.3.5) perform step 4.5), obtain target crossing numbering；
Step 4.3.6) remaining only element value in optional adjacency list, i.e. target crossing numbering；
Step 4.3.7) perform step 4.4), obtain target crossing numbering；
It is described do not turn around be with random routing strategy：
Step 4.4.1) removed from table adjacency wait select to vehicle it is next when crossing numbering, do not turn around；
Step 4.4.2) the random element returned in table adjacency.
Described shortest path section preference strategy is：
Step 4.5.1) inquire about all currently to select to crossing residing for vehicle as the length in the section of end points, contrast to obtain these
Section shortest length shortestLength；
Step 4.5.2) by it is all be end points currently to select to crossing residing for vehicle, length is equal to shortestLength
The numbering at another end points crossing in section be stored in list adjacency；
Step 4.5.3) obtain the current current length for selecting the optional adjacent crossing table adjaceney to crossing residing for vehicle
size；
Step 4.5.4) if size is equal to 1, perform step 4.5.5)；If size is more than 1, step 4.5.6 is performed)；
Step 4.5.5) remaining only element value in optional adjacency list, i.e. target crossing numbering；
Step 4.5.6) perform step 4.3), obtain target crossing numbering.
Beneficial effect：The present invention be given it is a kind of towards special vehicle based on avoiding the information physical of crowded prediction algorithm from melting
Syzygy system is selected to method, the method comprehensive utilization sensing technology, the communication technology, computing technique and control technology, make one, car, road
Interaction collaboration, the Scenario Design five for special vehicle crowded generation avoids crowded strategy, it is ensured that with police car, taxi
For the special vehicle for representing can dynamically be uniformly distributed under the constraint of existing road, form collection and calculate, communicate, being controlled in one
" overall situationization, intellectuality, mobilism, automation " select to method, so as to improve the dispatching efficiency of special vehicle.Meanwhile,
Due to introducing the routing factor in crowded strategy is avoided so that special vehicle is visible in all sections.In addition, it is to avoid crowded pre-
The time complexity of method of determining and calculating is low, disclosure satisfy that real-time in scheduling process towards the information physical emerging system of special vehicle
Property require.
(1) crowded tactful energy is avoided in actually select the Scenario Design of crowded generation during five for special vehicle
Special vehicle is enough set dynamically to be uniformly distributed under the constraint of existing road；
(2) the routing factor is introduced in crowded strategy is avoided, observability of the special vehicle in all sections can be optimized；
(3) random routing strategy can make the route on duty of special vehicle have certain disguise (can not in strategy four
It is predictive), at the same ensure that special vehicle each section select to when fairness (having equal opportunities)；
(4) avoid the time complexity of crowded prediction algorithm low, the information physical that disclosure satisfy that towards special vehicle is merged
Requirement of real-time of the system in scheduling process；
(5) information physical emerging system is introduced in the scheduling system towards special vehicle, intelligent transportation system is incorporated,
With certain advance；
(6) system employs modular design philosophy, there is preferable scalability and maintainability.
Brief description of the drawings
Fig. 1 is to be based on avoiding the special vehicle information physical emerging system of crowded prediction algorithm from selecting to illustraton of model,
Fig. 2 is special vehicle produced during crowded reason schematic diagram selecting,
Fig. 3 is to avoid crowded prediction algorithm flow chart.
Specific embodiment
Information physical emerging system of the present invention is by some physical locations (special vehicle, road conditions information gathering equipment
And user terminal) and information process unit (basic data and scheduler module) composition, physical location and information process unit
Communication interface is equipped with, is interacted by communication module.Physical location to information process unit send vehicle location, speed,
Section, vehicle direction, road conditions and call information, receive the control instruction from information process unit where vehicle；Information processing
Unit builds special vehicle Run-time scenario according to the information of vehicles, traffic information and call information combination basic data for receiving,
Each scene is carried out abstract and formulate corresponding strategy, and then scheduling control information is sent in real time to special vehicle.
Of the present invention being selected based on the special vehicle information physical emerging system for avoiding crowded prediction algorithm should to method
Used in selected towards the special vehicle with police car, taxi as representative to information physical emerging system in, select to strategy based on keeping away
Exempt from crowded prediction algorithm, when special vehicle drive to crossing wait select to when call the method.
The realization to method is selected including following based on the special vehicle information physical emerging system of crowded prediction algorithm is avoided
Step：
1st, the map datum (crossing coordinate, crossing syntople) that user provides, the adjacent crossing table array of generation are read
Adjacency, creates routing factor array selectionFactor.By all crossings number consecutively since 0 in map, often
An adjacent crossing table element in individual crossing correspondence one-dimension array adjacency, storage is adjacent with the crossing in adjacent crossing table
The all crossings numbering and adjacent crossing number for connecing.Unsigned long two-dimensional array selectionFactor is recorded (at one
In the cycle on duty that user specifies) (i.e. each bar section is corresponding to select for the number of times of special vehicle that passes through on each bar section in map
Path divisor value), all elements value is initially 0, and the routing factor pair in every section answers a selectionFactor element, selects
Path divisor value is numbered by two end points crossings of corresponding road section and retrieved in array selectionFactor, whenever Yi Tiaolu
There is special vehicle to pass through in section, the routing factor value in this section just adds 1, after an end cycle on duty, array
All elements value resets in selectionFactor, and element value is an integer in 0 to 264 in the array.User is read to carry
The configuration information of confession, remembers that the cycle on duty is T, and the step-length of special vehicle movement is STEP, and special vehicle is moved needed for a step-length
Time be t.System timer timer is initially 0, and all special vehicles often move forward a step-length STEP, and timer just increases
Plus t, when timer is equal to T (when reaching a cycle on duty), timer zero setting；
2nd, positional information, residing road section information (whole story crossing numbering) and the orientation information of all special vehicles are obtained, is deposited
Enter list specialVehicle.Traversal of lists specialVehical, note drives to the special vehicle at crossing to wait to select to spy
Vehicle is planted, is waited to select for first and is selected to vehicle for current to special vehicle, circulation performs step 3 and arrives step 5, is followed successively by these and waits to select
To special vehicle select to；
3rd, read from list specialVehicle and currently select crossing numbering u, u corresponding to vehicle number present positions
=specialVehicle.get (number) .position；
4th, it is entrance with strategy one, is the current numbering v for selecting and the target crossing to be sailed for being selected to vehicle；
5th, in routing factor array selectionFactor, the corresponding routing factor values of target road section are increased by 1,
selectionFactor[u][v]++.Write down one to wait to select to special vehicle currently to select to vehicle, be transferred to step 3.When all
Wait to select and all complete to select backward to special vehicle, all special vehicles move forward step-length STEP, timer a value for user configuring
Increase t.Judge the relation of timer and T, if timer is less than T, be transferred to step 2.Otherwise, step 6 is performed；
6th, an end cycle on duty, routing factor array selectionFactor resets, system timer zero setting.
Strategy one：Many cars are avoided to travel strategy in opposite directions.The scene that two car as shown in Figure 2 is travelled in opposite directions a, it is assumed that special type
Garage sails to crossing U, and in the three crossing { V abutted with crossing U1, V2, V3Middle selection crossing V1It is direction, section V1Have on U
Towards the special vehicle of U travelings, two cars are travelled in opposite directions.Two special purpose vehicle can be covered in response radius in this scene
Road section length occurs simultaneously maximum, and situation is the most crowded, in order to be able to be uniformly distributed special vehicle, should preferentially avoid the hair of this scene
It is raw.The choice step of strategy one is as follows：
1st, the current adjacent crossing table adjacency selected to crossing residing for vehicle is obtained；
2nd, obtain the numbering at the crossing of other special vehicle institute directions, judge successively these crossings whether be it is current select to
Crossing residing for vehicle, if so, the numbering u at corresponding special vehicle starting crossing is then searched, from adjacent crossing table adjacency
Remove initial crossing the numbering u, adjacency.remove (u)；
3rd, the current current length size for selecting the optional adjacent crossing table adjaceney to crossing residing for vehicle is obtained,
Size=adjacency.size ()；
If the 4, size is equal to 0, step 5 is performed；If size is equal to 1, step 6 is performed；If size is more than 1, hold
Row step 7；
5th, trigger policy five, obtain target crossing numbering v；
6th, remaining only element value in optional adjacency list, i.e. target crossing numbering v, v=adjacency.get (0)；
7th, trigger policy two, obtain target crossing numbering v；
Strategy two：The strategy for avoiding many cars from being travelled towards same crossing.Two car as shown in Figure 2 accompanies the scene of traveling, it is assumed that
One special purpose vehicle drives to crossing U, and in the three crossing { V abutted with crossing U1, V2, V3Middle selection crossing V1It is direction,
Identical section UV1It is upper to also have other special vehicles towards V1Traveling, two cars accompany traveling；Two car as shown in Figure 2 is towards with all the way
The scene of mouth traveling, in UV1Other sections V in addition1V4On have towards with crossing U on vehicle identical target crossing V1Traveling.
Both the above scene can all cause special vehicle to tend to congestion state, be avoided actually selecting also be tried one's best in.Strategy two is picked out
Select step as follows：
1st, all special vehicles are traveled through, whether the numbering v for judging the crossing of its direction successively is table adjacency
Element.If so, the numbering v at the crossing of correspondence vehicle institute direction is deleted from table adjacency then；
2nd, the current current length size for selecting the optional adjacent crossing table adjaceney to crossing residing for vehicle is obtained,
Size=adjacency.size ()；
If the 3, size is equal to 0, step 4 is performed；If size is equal to 1, step 5 is performed；If size is more than 1, hold
Row step 6；
4th, trigger policy five, obtain target crossing numbering v；
5th, remaining only element value in optional adjacency list, i.e. target crossing numbering v, v=adjacency.get (0)；
6th, trigger policy three, obtain target crossing numbering v；
Strategy three：Minimum routing factor induction strategies.As shown in Fig. 2 such situation is there is also in actually selecting, OK
The vehicle of crossing U is sailed in the three crossing { V abutted with U1, V2, V3In select to when, towards any crossing traveling all without leading
Crowded generation is caused, minimum routing factor induction strategies are now triggered, towards the sections of road that the correspondence routing factor is minimum.The strategy
On the basis of being dynamically uniformly distributed special vehicle is ensured, observability of the optimization special vehicle on all sections.Strategy
Three choice step is as follows：
1st, in inquiry table adjacency all respective stretch the routing factor, contrast to obtain the minimum routing factor
minFactor；
2nd, the end points crossing numbering in section of the corresponding routing factor more than minFactor in table adjacency is deleted；
3rd, the current current length size for selecting the optional adjacent crossing table adjaceney to crossing residing for vehicle is obtained,
Size=adjacency.size ()；
If the 4, size is equal to 0, step 5 is performed；If size is equal to 1, step 6 is performed；If size is more than 1, hold
Row step 7；
5th, trigger policy five, obtain target crossing numbering v；
6th, remaining only element value in optional adjacency list, i.e. target crossing numbering v, v=adjacency.get (0)；
7th, trigger policy four, obtain target crossing numbering v；
Strategy four：Do not turn around+random routing strategy.In the case that strategy fails more than, the strategy is triggered.Do not turn around
The purpose of strategy is to try to reduce the number of times that special vehicle comes and goes in a certain section；Random routing strategy can make special vehicle
Route on duty has certain disguise (unpredictability), at the same ensure that special vehicle each section select to when fairness
(having equal opportunities).
1st, removed from table adjacency wait select to vehicle it is next when crossing numbering, do not turn around；
2nd, the random element returned in table adjacency.
Strategy five：Shortest path section preference strategy.If as shown in Fig. 2 driving to the vehicle of crossing U in three abutted with U
Individual crossing { V1, V2, V3In select to when, can cause crowded generation towards each crossing traveling, then trigger the shortest path preferential plan of section
Slightly, selection towards the most short crossing traveling in the section and between U expects that at next crossing the choice of wisdom can be made, and breaks away from gather around as early as possible
Crowded state.
1st, inquire about all currently selecting to crossing residing for vehicle as the length in the section of end points, contrast these sections most
Short length shortestLength；
2nd, by it is all be end points currently to select to crossing residing for vehicle, length is equal to the section of shortestLength
The numbering at another end points crossing be stored in list adjacency；
3rd, the current current length size for selecting the optional adjacent crossing table adjaceney to crossing residing for vehicle is obtained,
Size=adjacency.size ()；
If the 4, size is equal to 1, step 5 is performed；If size is more than 1, step 6 is performed；
5th, remaining only element value in optional adjacency list, i.e. target crossing numbering v, v=adjacency.get (0)；
6th, trigger policy three, obtain target crossing numbering v.
Claims (6)
1. a kind of special vehicle information physical emerging system is selected to method, it is characterised in that the method is comprised the following steps：
Step 1) read the map datum including crossing coordinate, crossing syntople that user provides, the adjacent crossing table number of generation
Group adjacency, creates routing factor array selectionFactor；By all crossings number consecutively since 0 in map,
An adjacent crossing table element in the corresponding one-dimension array adjacency in each crossing, storage and the crossing in adjacent crossing table
Adjacent all crossings numbering and adjacent crossing number；The selectionFactor is unsigned long two-dimensional array, should
Array was recorded within the cycle on duty that a user specifies, and the number of times of the special vehicle passed through on each bar section in map is each bar
The corresponding routing factor values in section, all elements value is initially 0, and the routing factor pair in every section answers one
SelectionFactor elements, routing factor values are numbered in array by two end points crossings of corresponding road section
Retrieved in selectionFactor, whenever having special vehicle to pass through on a section, the routing factor value in this section just adds
1, after an end cycle on duty, all elements value resets in array selectionFactor, and element value is 0 to arrive in the array
264In an integer；Read the configuration information that user provides, including step-length STEP, the spy that cycle T on duty, special vehicle are moved
Kind of vehicle move a step-length needed for time t, setting system timer timer is simultaneously initially 0, and all special vehicles are every
Moving forward step-length a STEP, timer just increases t, and when timer is equal to T, that is, when reaching a cycle on duty, timer puts
Zero；
Step 2) obtain the positional information of all special vehicles, residing road section information and include that whole story crossing is numbered and orientation information,
It is stored in list specialVehicle；Traversal of lists specialVehical, note drive to the special vehicle at crossing for wait to select to
Special vehicle, sets first therein and waits to select to special vehicle currently to select to vehicle；
Step 3) read current selecting to the corresponding crossing numbering in vehicle present position；
Step 4) it is the current numbering selected and the target crossing to be sailed for is selected to vehicle, specifically chosen strategy includes：Avoid many
Car travels strategy-step 4.1 in opposite directions), avoid strategy-step 4.2 that many cars travel towards same crossing), routing factor minimum value
Preference strategy-step 4.3), do not turn around and random routing strategy-step 4.4), shortest path section preference strategy-step 4.5)；
Step 5) in routing factor array selectionFactor, the corresponding routing factor values of target road section are increased by 1；When
It need to be selected to special vehicle, writes down one and wait to select to special vehicle currently to select to vehicle, be transferred to step 3)；When needing to be selected
All complete to select to all special vehicles move forward step-length STEP, timer a value for user configuring to be increased to special vehicle
T, judges the relation of timer and T, if timer is less than T, is transferred to step 2), otherwise, perform step 6)；
Step 6) end cycle on duty, routing factor array selectionFactor resets, and system timer timer puts
Zero.
2. a kind of special vehicle information physical emerging system according to claim 1 is selected to method, it is characterised in that institute
The many cars that avoid stated travel strategy for step 4.1 in opposite directions)：
Step 4.1.1) obtain the current adjacent crossing table adjacency selected to crossing residing for vehicle；
Step 4.1.2) obtain other special vehicle institute directions crossing numbering, judge whether these crossings are currently to select successively
To the crossing residing for vehicle, if so, the numbering at corresponding special vehicle starting crossing is then searched, from adjacent crossing table adjacency
It is middle to remove the initial crossing numbering；
Step 4.1.3) obtain the current current length for selecting the optional adjacent crossing table adjaceney to crossing residing for vehicle
size；
Step 4.1.4) if size is equal to 0, perform step 4.1.5)；If size is equal to 1, step 4.1.6 is performed)；If
Size is more than 1, then perform step 4.1.7)；
Step 4.1.5) perform step 4.5), obtain target crossing numbering；
Step 4.1.6) remaining only element value in optional adjacency list, i.e. target crossing numbering；
Step 4.1.7) perform step 4.2), obtain target crossing numbering.
3. a kind of special vehicle information physical emerging system according to claim 1 is selected to method, it is characterised in that institute
The strategy that many cars are travelled towards same crossing that avoids stated is step 4.2)：
Step 4.2.1) all special vehicles are traveled through, whether the numbering for judging the crossing of its direction successively is table adjacency
Element, if so, the numbering at crossing of correspondence vehicle institute direction is deleted from table adjacency then；
Step 4.2.2) obtain the current current length for selecting the optional adjacent crossing table adjaceney to crossing residing for vehicle
size；
Step 4.2.3) if size is equal to 0, perform step 4.2.4)；If size is equal to 1, step 4.2.5 is performed)；If
Size is more than 1, then perform step 4.2.6)；
Step 4.2.4) perform step 4.5), obtain target crossing numbering；
Step 4.2.5) remaining only element value in optional adjacency list, i.e. target crossing numbering；
Step 4.2.6) perform step 4.3), obtain target crossing numbering.
4. a kind of special vehicle information physical emerging system according to claim 1 is selected to method, it is characterised in that institute
The routing factor minimum value preference strategy stated is step 4.3)：
Step 4.3.1) the routing factor of all respective stretch in inquiry table adjacency, contrast to obtain the minimum routing factor
minFactor；
Step 4.3.2) delete the end points crossing volume that the corresponding routing factor in table adjacency is more than the section of minFactor
Number；
Step 4.3.3) obtain the current current length for selecting the optional adjacent crossing table adjaceney to crossing residing for vehicle
size；
Step 4.3.4) if size is equal to 0, perform step 4.3.5)；If size is equal to 1, step 4.3.6 is performed)；If
Size is more than 1, then perform step 4.3.7)；
Step 4.3.5) perform step 4.5), obtain target crossing numbering；
Step 4.3.6) remaining only element value in optional adjacency list, i.e. target crossing numbering；
Step 4.3.7) perform step 4.4), obtain target crossing numbering.
5. a kind of special vehicle information physical emerging system according to claim 1 is selected to method, it is characterised in that institute
Do not turn around and the random routing strategy stated are step 4.4)：
Step 4.4.1) removed from table adjacency wait select to vehicle it is next when crossing numbering, do not turn around；
Step 4.4.2) the random element returned in table adjacency.
6. a kind of special vehicle information physical emerging system according to claim 1 is selected to method, it is characterised in that institute
The shortest path section preference strategy stated is step 4.5)：
Step 4.5.1) inquire about all currently to select to crossing residing for vehicle as the length in the section of end points, contrast to obtain these sections
Shortest length shortestLength；
Step 4.5.2) by it is all be end points currently to select to crossing residing for vehicle, length is equal to the road of shortestLength
The numbering at another end points crossing of section is stored in list adjacency；
Step 4.5.3) obtain the current current length for selecting the optional adjacent crossing table adjaceney to crossing residing for vehicle
size；
Step 4.5.4) if size is equal to 1, perform step 4.5.5)；If size is more than 1, step 4.5.6 is performed)；
Step 4.5.5) remaining only element value in optional adjacency list, i.e. target crossing numbering；
Step 4.5.6) perform step 4.3), obtain target crossing numbering.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510232737.0A CN104966406B (en) | 2015-05-08 | 2015-05-08 | A kind of special vehicle information physical emerging system is selected to method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510232737.0A CN104966406B (en) | 2015-05-08 | 2015-05-08 | A kind of special vehicle information physical emerging system is selected to method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104966406A CN104966406A (en) | 2015-10-07 |
| CN104966406B true CN104966406B (en) | 2017-06-30 |
Family
ID=54220437
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510232737.0A Expired - Fee Related CN104966406B (en) | 2015-05-08 | 2015-05-08 | A kind of special vehicle information physical emerging system is selected to method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104966406B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105321362B (en) * | 2015-10-30 | 2017-10-13 | 湖南大学 | A kind of intelligent coordinated passing method of intersection vehicles |
| CN114530044B (en) * | 2022-02-24 | 2023-08-01 | 支付宝(杭州)信息技术有限公司 | Vehicle running scheduling processing method and device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101256083A (en) * | 2008-04-09 | 2008-09-03 | 山东大学 | Method for selecting urban traffic network path based on dynamic information |
| CN103253147A (en) * | 2013-05-11 | 2013-08-21 | 安徽工程大学 | Solar energy all-electric patrol car |
| TW201418075A (en) * | 2012-11-07 | 2014-05-16 | Univ Far East | Automatic ambulance guiding device for car accident |
| JP2014130516A (en) * | 2012-12-28 | 2014-07-10 | Aso Information System Co Ltd | Total support system for rescue work |
| CN103955804A (en) * | 2014-05-20 | 2014-07-30 | 中山大学 | Crime risk spatial-temporal pattern recognition method serving policing prevention and control district planning |
-
2015
- 2015-05-08 CN CN201510232737.0A patent/CN104966406B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101256083A (en) * | 2008-04-09 | 2008-09-03 | 山东大学 | Method for selecting urban traffic network path based on dynamic information |
| TW201418075A (en) * | 2012-11-07 | 2014-05-16 | Univ Far East | Automatic ambulance guiding device for car accident |
| JP2014130516A (en) * | 2012-12-28 | 2014-07-10 | Aso Information System Co Ltd | Total support system for rescue work |
| CN103253147A (en) * | 2013-05-11 | 2013-08-21 | 安徽工程大学 | Solar energy all-electric patrol car |
| CN103955804A (en) * | 2014-05-20 | 2014-07-30 | 中山大学 | Crime risk spatial-temporal pattern recognition method serving policing prevention and control district planning |
Non-Patent Citations (1)
| Title |
|---|
| 基于自适应规则的择路演化研究;王谱 等;《交通运输系统工程与信息》;20100831;第10卷(第4期);第86-92页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104966406A (en) | 2015-10-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106251016B (en) | A kind of parking system paths planning method based on dynamic time windows | |
| CN107389076A (en) | A kind of real-time dynamic path planning method of energy-conservation suitable for intelligent network connection automobile | |
| CN111369181B (en) | Train autonomous scheduling deep reinforcement learning method and device | |
| CN106875710B (en) | A kind of intersection self-organization control method towards net connection automatic driving vehicle | |
| CN104240012B (en) | Charging electric vehicle dispatching method and system based on Internet of Things | |
| Chen et al. | An intelligent path planning scheme of autonomous vehicles platoon using deep reinforcement learning on network edge | |
| CN104766484B (en) | Traffic Control and Guidance system and method based on Evolutionary multiobjective optimization and ant group algorithm | |
| Zhang et al. | Eco-driving control for connected and automated electric vehicles at signalized intersections with wireless charging | |
| CN114495527B (en) | Internet-connected intersection vehicle road collaborative optimization method and system in mixed traffic environment | |
| CN107766994A (en) | A kind of shared bicycle dispatching method and scheduling system | |
| Wang et al. | Multi-objective optimization of real-time customized bus routes based on two-stage method | |
| CN105243856B (en) | The self-organizing construction method of urban traffic signal intelligent control rule | |
| Wu et al. | Smart fog based workflow for traffic control networks | |
| CN107180282A (en) | A kind of peak period many train energy conservation optimizing methods | |
| Lin et al. | Traffic signal optimization based on fuzzy control and differential evolution algorithm | |
| CN108877253A (en) | A kind of public transportation lane resource dynamic sharing method and system based on Internet of Things | |
| CN1207693C (en) | Automatic unit based self organizing and controlling method for urban traffic signals | |
| CN102867409A (en) | Road traffic cooperative control method for urban central area | |
| CN108592928A (en) | Construction method and device for double-layer path of vehicle-mounted unmanned aerial vehicle | |
| CN104966406B (en) | A kind of special vehicle information physical emerging system is selected to method | |
| CN113762780A (en) | Method for processing medical waste collection problem by using improved genetic algorithm | |
| CN106373384A (en) | Remote area passenger transport regular bus route real-time generation method | |
| Jia et al. | Analysis of the development status of artificial intelligence technology at home and abroad | |
| Wang et al. | Simulation of multi-agent based cybernetic transportation system | |
| Lin et al. | The Intelligent Charging Path Planning for Electric Vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20170630 |